Estimating the failure rate of the log-logistic distribution by smooth adaptive and bias-correction methods
學年 109
學期 2
出版(發表)日期 2021-06-01
作品名稱 Estimating the failure rate of the log-logistic distribution by smooth adaptive and bias-correction methods
作品名稱(其他語言)
著者 Xi Zheng; Jyun-You Chiang; Tzong-Ru Tsai; Shuai Wanga
單位
出版者
著錄名稱、卷期、頁數 Computers & Industrial Engineering 156, 107188
摘要 The Log-logistic distribution has successfully earned attention in practical applications due to its good statistical properties. Because the traditional maximum likelihood estimators of the Log-logistic distribution parameters do not have an explicit form and are biased when the sample size is small. Therefore, the estimation and prediction of the failure rate is not well. In this study, we study the quality of the maximum likelihood, asymptotic maximum likelihood and bias-corrected maximum likelihood methods, and propose a smooth adaptive estimation method for estimating the Log-logistic distribution parameters. To reduce the bias of the asymptotic maximum likelihood and smooth adaptive estimators of the Log-logistic distribution parameters, the bias-corrected method is used to improve the asymptotic maximum likelihood and smooth adaptive estimation methods. Two new bias-corrected estimation methods are also proposed to obtain reliable estimates of the Log-logistic distribution parameters. An intensive Monte Carlo simulation study is conducted to evaluate the performance of these estimation methods. Simulation results show that the smooth adaptive and two new bias-corrected estimation methods are more competitive than other competitors. Finally, two real example is used for illustrating the applications of the smooth adaptive, CAML and CSA estimation methods.
關鍵字 Log-logistic distribution;Smooth adaptive method;Failure rate;Bias reduction;Maximum likelihood estimation
語言 en_US
ISSN 0360-8352
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度
國別 GBR
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120459 )

SDGS 優質教育,產業創新與基礎設施